14_SimpleStockClasses.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Simple Stock Classes and Objects"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## This tutorial teaches how to write simple classes and objects for stock."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Library\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\") \n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AAPL'\n",
        "start = '2014-01-01'\n",
        "end = '2019-01-01'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close     Volume\n",
              "Date                                                                        \n",
              "2014-01-02  79.382858  79.575714  78.860001  79.018570  71.591667   58671200\n",
              "2014-01-03  78.980003  79.099998  77.204285  77.282860  70.019096   98116900\n",
              "2014-01-06  76.778572  78.114288  76.228569  77.704285  70.400902  103152700\n",
              "2014-01-07  77.760002  77.994286  76.845711  77.148575  69.897423   79302300\n",
              "2014-01-08  76.972855  77.937141  76.955711  77.637146  70.340096   64632400"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>79.382858</td>\n",
              "      <td>79.575714</td>\n",
              "      <td>78.860001</td>\n",
              "      <td>79.018570</td>\n",
              "      <td>71.591667</td>\n",
              "      <td>58671200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>78.980003</td>\n",
              "      <td>79.099998</td>\n",
              "      <td>77.204285</td>\n",
              "      <td>77.282860</td>\n",
              "      <td>70.019096</td>\n",
              "      <td>98116900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>76.778572</td>\n",
              "      <td>78.114288</td>\n",
              "      <td>76.228569</td>\n",
              "      <td>77.704285</td>\n",
              "      <td>70.400902</td>\n",
              "      <td>103152700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>77.760002</td>\n",
              "      <td>77.994286</td>\n",
              "      <td>76.845711</td>\n",
              "      <td>77.148575</td>\n",
              "      <td>69.897423</td>\n",
              "      <td>79302300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>76.972855</td>\n",
              "      <td>77.937141</td>\n",
              "      <td>76.955711</td>\n",
              "      <td>77.637146</td>\n",
              "      <td>70.340096</td>\n",
              "      <td>64632400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create Basic Class"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "class stock_data:\n",
        "    def __init__(self):\n",
        "        pass\n",
        "    \n",
        "    def function(self):\n",
        "        print(\"Here my stock data:\\n\", dataset)"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "myobject = stock_data\n",
        "print(myobject.function(dataset))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Here my stock data:\n",
            "                   Open        High         Low       Close   Adj Close  \\\n",
            "Date                                                                     \n",
            "2014-01-02   79.382858   79.575714   78.860001   79.018570   71.591667   \n",
            "2014-01-03   78.980003   79.099998   77.204285   77.282860   70.019096   \n",
            "2014-01-06   76.778572   78.114288   76.228569   77.704285   70.400902   \n",
            "2014-01-07   77.760002   77.994286   76.845711   77.148575   69.897423   \n",
            "2014-01-08   76.972855   77.937141   76.955711   77.637146   70.340096   \n",
            "2014-01-09   78.114288   78.122856   76.478569   76.645714   69.441841   \n",
            "2014-01-10   77.118568   77.257141   75.872856   76.134285   68.978462   \n",
            "2014-01-13   75.701431   77.500000   75.697144   76.532860   69.339584   \n",
            "2014-01-14   76.888573   78.104286   76.808571   78.055717   70.719322   \n",
            "2014-01-15   79.074287   80.028572   78.808571   79.622856   72.139168   \n",
            "2014-01-16   79.271431   79.550003   78.811432   79.178574   71.736649   \n",
            "2014-01-17   78.782860   78.867142   77.128571   77.238571   69.978973   \n",
            "2014-01-21   77.284286   78.581429   77.202858   78.438568   71.066185   \n",
            "2014-01-22   78.701431   79.612854   78.258568   78.787140   71.382004   \n",
            "2014-01-23   78.562859   79.500000   77.830002   79.454285   71.986450   \n",
            "2014-01-24   79.142860   79.374283   77.821426   78.010002   70.677895   \n",
            "2014-01-27   78.581429   79.257141   77.964287   78.642860   71.251266   \n",
            "2014-01-28   72.680000   73.571426   71.724289   72.357140   65.556343   \n",
            "2014-01-29   71.992859   72.481430   71.231430   71.535713   64.812126   \n",
            "2014-01-30   71.791428   72.357140   70.957146   71.397141   64.686554   \n",
            "2014-01-31   70.739998   71.647141   70.507141   71.514282   64.792686   \n",
            "2014-02-03   71.801430   72.532860   71.328575   71.647141   64.913078   \n",
            "2014-02-04   72.264282   72.779999   71.822861   72.684288   65.852753   \n",
            "2014-02-05   72.365715   73.611427   72.321426   73.227142   66.344574   \n",
            "2014-02-06   72.865715   73.357140   72.544289   73.215714   66.731277   \n",
            "2014-02-07   74.482857   74.704285   73.911430   74.239998   67.664833   \n",
            "2014-02-10   74.094284   75.998573   74.000000   75.570000   68.877052   \n",
            "2014-02-11   75.801430   76.821426   75.642860   76.565712   69.784576   \n",
            "2014-02-12   76.707146   77.080002   76.177139   76.559998   69.779381   \n",
            "2014-02-13   76.379997   77.835716   76.314285   77.775711   70.887421   \n",
            "...                ...         ...         ...         ...         ...   \n",
            "2018-11-15  188.389999  191.970001  186.899994  191.410004  189.861435   \n",
            "2018-11-16  190.500000  194.970001  189.460007  193.529999  191.964279   \n",
            "2018-11-19  190.000000  190.699997  184.990005  185.860001  184.356339   \n",
            "2018-11-20  178.369995  181.470001  175.509995  176.979996  175.548157   \n",
            "2018-11-21  179.729996  180.270004  176.550003  176.779999  175.349792   \n",
            "2018-11-23  174.940002  176.600006  172.100006  172.289993  170.896118   \n",
            "2018-11-26  174.240005  174.949997  170.259995  174.619995  173.207260   \n",
            "2018-11-27  171.509995  174.770004  170.880005  174.240005  172.830338   \n",
            "2018-11-28  176.729996  181.289993  174.929993  180.940002  179.476135   \n",
            "2018-11-29  182.660004  182.800003  177.699997  179.550003  178.097382   \n",
            "2018-11-30  180.289993  180.330002  177.029999  178.580002  177.135223   \n",
            "2018-12-03  184.460007  184.940002  181.210007  184.820007  183.324753   \n",
            "2018-12-04  180.949997  182.389999  176.270004  176.690002  175.260513   \n",
            "2018-12-06  171.759995  174.779999  170.419998  174.720001  173.306473   \n",
            "2018-12-07  173.490005  174.490005  168.300003  168.490005  167.126862   \n",
            "2018-12-10  165.000000  170.089996  163.330002  169.600006  168.227890   \n",
            "2018-12-11  171.660004  171.789993  167.000000  168.630005  167.265732   \n",
            "2018-12-12  170.399994  171.919998  169.020004  169.100006  167.731934   \n",
            "2018-12-13  170.490005  172.570007  169.550003  170.949997  169.566956   \n",
            "2018-12-14  169.000000  169.080002  165.279999  165.479996  164.141220   \n",
            "2018-12-17  165.449997  168.350006  162.729996  163.940002  162.613678   \n",
            "2018-12-18  165.380005  167.529999  164.389999  166.070007  164.726440   \n",
            "2018-12-19  166.000000  167.449997  159.089996  160.889999  159.588348   \n",
            "2018-12-20  160.399994  162.110001  155.300003  156.830002  155.561188   \n",
            "2018-12-21  156.860001  158.160004  149.630005  150.729996  149.510544   \n",
            "2018-12-24  148.149994  151.550003  146.589996  146.830002  145.642090   \n",
            "2018-12-26  148.300003  157.229996  146.720001  157.169998  155.898438   \n",
            "2018-12-27  155.839996  156.770004  150.070007  156.149994  154.886688   \n",
            "2018-12-28  157.500000  158.520004  154.550003  156.229996  154.966034   \n",
            "2018-12-31  158.529999  159.360001  156.479996  157.740005  156.463837   \n",
            "\n",
            "               Volume  \n",
            "Date                   \n",
            "2014-01-02   58671200  \n",
            "2014-01-03   98116900  \n",
            "2014-01-06  103152700  \n",
            "2014-01-07   79302300  \n",
            "2014-01-08   64632400  \n",
            "2014-01-09   69787200  \n",
            "2014-01-10   76244000  \n",
            "2014-01-13   94623200  \n",
            "2014-01-14   83140400  \n",
            "2014-01-15   97909700  \n",
            "2014-01-16   57319500  \n",
            "2014-01-17  106684900  \n",
            "2014-01-21   82131700  \n",
            "2014-01-22   94996300  \n",
            "2014-01-23  100809800  \n",
            "2014-01-24  107338700  \n",
            "2014-01-27  138719700  \n",
            "2014-01-28  266380800  \n",
            "2014-01-29  125702500  \n",
            "2014-01-30  169625400  \n",
            "2014-01-31  116199300  \n",
            "2014-02-03  100366000  \n",
            "2014-02-04   94170300  \n",
            "2014-02-05   82086200  \n",
            "2014-02-06   64441300  \n",
            "2014-02-07   92570100  \n",
            "2014-02-10   86389800  \n",
            "2014-02-11   70564200  \n",
            "2014-02-12   77025200  \n",
            "2014-02-13   76849500  \n",
            "...               ...  \n",
            "2018-11-15   46478800  \n",
            "2018-11-16   36928300  \n",
            "2018-11-19   41925300  \n",
            "2018-11-20   67825200  \n",
            "2018-11-21   31124200  \n",
            "2018-11-23   23624000  \n",
            "2018-11-26   44998500  \n",
            "2018-11-27   41387400  \n",
            "2018-11-28   46062500  \n",
            "2018-11-29   41770000  \n",
            "2018-11-30   39531500  \n",
            "2018-12-03   40802500  \n",
            "2018-12-04   41344300  \n",
            "2018-12-06   43098400  \n",
            "2018-12-07   42281600  \n",
            "2018-12-10   62026000  \n",
            "2018-12-11   47281700  \n",
            "2018-12-12   35627700  \n",
            "2018-12-13   31898600  \n",
            "2018-12-14   40703700  \n",
            "2018-12-17   44287900  \n",
            "2018-12-18   33841500  \n",
            "2018-12-19   49047300  \n",
            "2018-12-20   64773000  \n",
            "2018-12-21   95744600  \n",
            "2018-12-24   37169200  \n",
            "2018-12-26   58582500  \n",
            "2018-12-27   53117100  \n",
            "2018-12-28   42291400  \n",
            "2018-12-31   35003500  \n",
            "\n",
            "[1258 rows x 6 columns]\n",
            "None\n"
          ]
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create Class of Exploratory Data"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Create class\n",
        "class ExploratoryData:\n",
        "    # Initialize class with self argument\n",
        "    def __init__(self):\n",
        "        pass\n",
        "    \n",
        "    # Define class method which takes self argument: print_stock_summary\n",
        "    def print_stock_summary(self):\n",
        "        # Print string\n",
        "        print(\"__________________Exploratory Data Analysis__________________\")\n",
        "        print(\"Stock Data\")\n",
        "        print('-'*60)\n",
        "        print(\"Dataset information\") \n",
        "        print(dataset.info(memory_usage='deep',verbose=False))\n",
        "        print('-'*60)\n",
        "        print(dataset.info())\n",
        "        print('-'*60)\n",
        "        print(\"Data type:\")\n",
        "        print(dataset.dtypes)\n",
        "        print('-'*60)\n",
        "        print(\"Check unique values wihtout NaN\")\n",
        "        print(dataset.nunique())\n",
        "        print('-'*60)\n",
        "        print(\"Data shape:\")\n",
        "        print(dataset.shape)\n",
        "        print('-'*60)\n",
        "        print(\"Data columns Names:\")\n",
        "        print(dataset.columns)\n",
        "        print('-'*60)\n",
        "        print(\"Check for NaNs:\")\n",
        "        print(dataset.isnull().values.any())\n",
        "        print(\"Check for NaNs in rows:\")\n",
        "        print(dataset.isnull().sum(axis = 0))\n",
        "        print(\"Check for NaNs in columns:\")\n",
        "        print(dataset.isnull().sum(axis = 1))\n",
        "        print('-'*60)\n",
        "        print(\"Data Statistics Summary:\")\n",
        "        print(dataset.describe())"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ExploratoryData.print_stock_summary(dataset)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "__________________Exploratory Data Analysis__________________\n",
            "Stock Data\n",
            "------------------------------------------------------------\n",
            "Dataset information\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 1258 entries, 2014-01-02 to 2018-12-31\n",
            "Columns: 6 entries, Open to Volume\n",
            "dtypes: float64(5), int32(1)\n",
            "memory usage: 63.9 KB\n",
            "None\n",
            "------------------------------------------------------------\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 1258 entries, 2014-01-02 to 2018-12-31\n",
            "Data columns (total 6 columns):\n",
            "Open         1258 non-null float64\n",
            "High         1258 non-null float64\n",
            "Low          1258 non-null float64\n",
            "Close        1258 non-null float64\n",
            "Adj Close    1258 non-null float64\n",
            "Volume       1258 non-null int32\n",
            "dtypes: float64(5), int32(1)\n",
            "memory usage: 63.9 KB\n",
            "None\n",
            "------------------------------------------------------------\n",
            "Data type:\n",
            "Open         float64\n",
            "High         float64\n",
            "Low          float64\n",
            "Close        float64\n",
            "Adj Close    float64\n",
            "Volume         int32\n",
            "dtype: object\n",
            "------------------------------------------------------------\n",
            "Check unique values wihtout NaN\n",
            "Open         1184\n",
            "High         1179\n",
            "Low          1180\n",
            "Close        1170\n",
            "Adj Close    1232\n",
            "Volume       1255\n",
            "dtype: int64\n",
            "------------------------------------------------------------\n",
            "Data shape:\n",
            "(1258, 6)\n",
            "------------------------------------------------------------\n",
            "Data columns Names:\n",
            "Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n",
            "------------------------------------------------------------\n",
            "Check for NaNs:\n",
            "False\n",
            "Check for NaNs in rows:\n",
            "Open         0\n",
            "High         0\n",
            "Low          0\n",
            "Close        0\n",
            "Adj Close    0\n",
            "Volume       0\n",
            "dtype: int64\n",
            "Check for NaNs in columns:\n",
            "Date\n",
            "2014-01-02    0\n",
            "2014-01-03    0\n",
            "2014-01-06    0\n",
            "2014-01-07    0\n",
            "2014-01-08    0\n",
            "2014-01-09    0\n",
            "2014-01-10    0\n",
            "2014-01-13    0\n",
            "2014-01-14    0\n",
            "2014-01-15    0\n",
            "2014-01-16    0\n",
            "2014-01-17    0\n",
            "2014-01-21    0\n",
            "2014-01-22    0\n",
            "2014-01-23    0\n",
            "2014-01-24    0\n",
            "2014-01-27    0\n",
            "2014-01-28    0\n",
            "2014-01-29    0\n",
            "2014-01-30    0\n",
            "2014-01-31    0\n",
            "2014-02-03    0\n",
            "2014-02-04    0\n",
            "2014-02-05    0\n",
            "2014-02-06    0\n",
            "2014-02-07    0\n",
            "2014-02-10    0\n",
            "2014-02-11    0\n",
            "2014-02-12    0\n",
            "2014-02-13    0\n",
            "             ..\n",
            "2018-11-15    0\n",
            "2018-11-16    0\n",
            "2018-11-19    0\n",
            "2018-11-20    0\n",
            "2018-11-21    0\n",
            "2018-11-23    0\n",
            "2018-11-26    0\n",
            "2018-11-27    0\n",
            "2018-11-28    0\n",
            "2018-11-29    0\n",
            "2018-11-30    0\n",
            "2018-12-03    0\n",
            "2018-12-04    0\n",
            "2018-12-06    0\n",
            "2018-12-07    0\n",
            "2018-12-10    0\n",
            "2018-12-11    0\n",
            "2018-12-12    0\n",
            "2018-12-13    0\n",
            "2018-12-14    0\n",
            "2018-12-17    0\n",
            "2018-12-18    0\n",
            "2018-12-19    0\n",
            "2018-12-20    0\n",
            "2018-12-21    0\n",
            "2018-12-24    0\n",
            "2018-12-26    0\n",
            "2018-12-27    0\n",
            "2018-12-28    0\n",
            "2018-12-31    0\n",
            "Length: 1258, dtype: int64\n",
            "------------------------------------------------------------\n",
            "Data Statistics Summary:\n",
            "              Open         High          Low        Close    Adj Close  \\\n",
            "count  1258.000000  1258.000000  1258.000000  1258.000000  1258.000000   \n",
            "mean    131.227685   132.355248   130.079289   131.241425   125.672176   \n",
            "std      37.428791    37.771772    37.064776    37.414122    38.863625   \n",
            "min      70.739998    71.647141    70.507141    71.397141    64.686554   \n",
            "25%     102.900000   104.374998   102.440003   103.037502    96.752972   \n",
            "50%     118.940002   119.889999   118.215000   119.180001   112.564926   \n",
            "75%     158.522499   159.990002   156.707500   157.830002   154.546783   \n",
            "max     230.779999   233.470001   229.779999   232.070007   229.392090   \n",
            "\n",
            "             Volume  \n",
            "count  1.258000e+03  \n",
            "mean   4.293002e+07  \n",
            "std    2.358282e+07  \n",
            "min    1.147590e+07  \n",
            "25%    2.660325e+07  \n",
            "50%    3.694200e+07  \n",
            "75%    5.234740e+07  \n",
            "max    2.663808e+08  \n"
          ]
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create Class of daily returns and plot histogram"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Create class: DataShell\n",
        "class StockReturns:\n",
        "  \n",
        "\t# Initialize class with self and stockData as arguments\n",
        "    def __init__(self, stockData):\n",
        "      \t# Set data as instance variable, and assign it the value of stockData\n",
        "        self.data = stockData\n",
        "    \n",
        "    # Define Log Returns\n",
        "    def stock_log(self):\n",
        "        log_returns = np.log(dataset['Adj Close'].shift(-1)) - np.log(dataset['Adj Close'])\n",
        "        return log_returns\n",
        "    \n",
        "\t# Define MU\n",
        "    def stock_mu(self):\n",
        "        log_returns = np.log(dataset['Adj Close'].shift(-1)) - np.log(dataset['Adj Close'])\n",
        "        mu = log_returns.mean()\n",
        "        return mu\n",
        "        \n",
        "    # Define Sigma\n",
        "    def stock_sigma(self):\n",
        "        log_returns = np.log(dataset['Adj Close'].shift(-1)) - np.log(dataset['Adj Close'])\n",
        "        sigma = log_returns.std(ddof=1)\n",
        "        return sigma"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock_data = StockReturns(dataset)"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock_data.stock_log()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": [
              "Date\n",
              "2014-01-02   -0.022211\n",
              "2014-01-03    0.005438\n",
              "2014-01-06   -0.007177\n",
              "2014-01-07    0.006313\n",
              "2014-01-08   -0.012852\n",
              "2014-01-09   -0.006695\n",
              "2014-01-10    0.005222\n",
              "2014-01-13    0.019703\n",
              "2014-01-14    0.019878\n",
              "2014-01-15   -0.005595\n",
              "2014-01-16   -0.024807\n",
              "2014-01-17    0.015417\n",
              "2014-01-21    0.004434\n",
              "2014-01-22    0.008432\n",
              "2014-01-23   -0.018345\n",
              "2014-01-24    0.008080\n",
              "2014-01-27   -0.083303\n",
              "2014-01-28   -0.011417\n",
              "2014-01-29   -0.001939\n",
              "2014-01-30    0.001639\n",
              "2014-01-31    0.001856\n",
              "2014-02-03    0.014372\n",
              "2014-02-04    0.007441\n",
              "2014-02-05    0.005812\n",
              "2014-02-06    0.013893\n",
              "2014-02-07    0.017756\n",
              "2014-02-10    0.013090\n",
              "2014-02-11   -0.000074\n",
              "2014-02-12    0.015754\n",
              "2014-02-13   -0.000808\n",
              "                ...   \n",
              "2018-11-15    0.011015\n",
              "2018-11-16   -0.040439\n",
              "2018-11-19   -0.048957\n",
              "2018-11-20   -0.001131\n",
              "2018-11-21   -0.025727\n",
              "2018-11-23    0.013433\n",
              "2018-11-26   -0.002179\n",
              "2018-11-27    0.037732\n",
              "2018-11-28   -0.007712\n",
              "2018-11-29   -0.005417\n",
              "2018-11-30    0.034346\n",
              "2018-12-03   -0.044986\n",
              "2018-12-04   -0.011212\n",
              "2018-12-06   -0.036308\n",
              "2018-12-07    0.006566\n",
              "2018-12-10   -0.005736\n",
              "2018-12-11    0.002783\n",
              "2018-12-12    0.010881\n",
              "2018-12-13   -0.032521\n",
              "2018-12-14   -0.009350\n",
              "2018-12-17    0.012909\n",
              "2018-12-18   -0.031688\n",
              "2018-12-19   -0.025559\n",
              "2018-12-20   -0.039672\n",
              "2018-12-21   -0.026215\n",
              "2018-12-24    0.068053\n",
              "2018-12-26   -0.006511\n",
              "2018-12-27    0.000512\n",
              "2018-12-28    0.009619\n",
              "2018-12-31         NaN\n",
              "Name: Adj Close, Length: 1258, dtype: float64"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock_data.stock_log().head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": [
              "Date\n",
              "2014-01-02   -0.022211\n",
              "2014-01-03    0.005438\n",
              "2014-01-06   -0.007177\n",
              "2014-01-07    0.006313\n",
              "2014-01-08   -0.012852\n",
              "Name: Adj Close, dtype: float64"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock_data.stock_mu()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": [
              "0.0006219938148640764"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock_data.stock_sigma()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": [
              "0.015077566404571254"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create Class of Family"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Define Class of family for stock\n",
        "class Stock_Data:\n",
        "    # Stock_Family = 'DataStock'\n",
        "    def __init__(self): \n",
        "        self.data = dataset\n",
        "\n",
        "# Define class Plot_Line\n",
        "class Plot_Line(Stock_Data):\n",
        "    def __init__(self):\n",
        "        f = plt.figure(figsize=(14,10))\n",
        "        plt.plot(self.data['Adj Close'])\n",
        "        plt.legend(loc='best')\n",
        "        plt.title('Stock Closing Price')\n",
        "        plt.xlabel('Date')\n",
        "        plt.ylabel('Price')\n",
        "        return f\n",
        "    \n",
        "# Define class Plot_hist\n",
        "class Plot_hist(Stock_Data):\n",
        "    def __init__(self):\n",
        "        f = plt.figure(figsize=(14,10))\n",
        "        plt.hist(self.data['Adj Close'])\n",
        "        plt.legend(loc='best')\n",
        "        plt.title('Stock Closing Price')\n",
        "        plt.xlabel('Date')\n",
        "        plt.ylabel('Price')\n",
        "        return f"
      ],
      "outputs": [],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Stock_Data(object):\n",
        "    def __init__(self): \n",
        "        pass\n",
        "      \n",
        "    # Define class Plot_Line\n",
        "    def Plot_Line(self):\n",
        "        plt.figure(figsize=(14,10))\n",
        "        plt.plot(dataset['Adj Close'])\n",
        "        plt.legend(loc='best')\n",
        "        plt.title('Stock Closing Price')\n",
        "        plt.xlabel('Date')\n",
        "        plt.ylabel('Price')\n",
        "        return \n",
        "    \n",
        "    # Define class Plot_hist\n",
        "    def Plot_hist(sef):\n",
        "        f = plt.figure(figsize=(14,10))\n",
        "        plt.hist(dataset['Adj Close'])\n",
        "        plt.title('Stock Closing Price')\n",
        "        plt.xlabel('Date')\n",
        "        plt.ylabel('Price')\n",
        "        return"
      ],
      "outputs": [],
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Stock_Data.Plot_Line(dataset)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ],
            "image/png": [
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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Stock_Data.Plot_hist(dataset)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classes and Instances"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "class Stock:\n",
        "\t# Initialize class with self, symbol, start, and end as arguments\n",
        "    def __init__(self, symbol, start, end):\n",
        "      \t# Set data as instance variable, and assign it the variables of stock\n",
        "        self.symbol = symbol\n",
        "        self.start = start\n",
        "        self.end = end\n",
        "    \n",
        "    def get_descriptive_data(self):\n",
        "        dataset = yf.download(self.symbol,self.start,self.end)\n",
        "        return dataset"
      ],
      "outputs": [],
      "execution_count": 17,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = Stock('AMD','2014-01-01', '2019-01-01')\n",
        "print(df.get_descriptive_data())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "                 Open       High        Low      Close  Adj Close     Volume\n",
            "Date                                                                        \n",
            "2014-01-02   3.850000   3.980000   3.840000   3.950000   3.950000   20548400\n",
            "2014-01-03   3.980000   4.000000   3.880000   4.000000   4.000000   22887200\n",
            "2014-01-06   4.010000   4.180000   3.990000   4.130000   4.130000   42398300\n",
            "2014-01-07   4.190000   4.250000   4.110000   4.180000   4.180000   42932100\n",
            "2014-01-08   4.230000   4.260000   4.140000   4.180000   4.180000   30678700\n",
            "2014-01-09   4.200000   4.230000   4.050000   4.090000   4.090000   30667600\n",
            "2014-01-10   4.090000   4.200000   4.070000   4.170000   4.170000   20840800\n",
            "2014-01-13   4.190000   4.200000   4.090000   4.130000   4.130000   22856100\n",
            "2014-01-14   4.140000   4.300000   4.130000   4.300000   4.300000   42434800\n",
            "2014-01-15   4.340000   4.600000   4.320000   4.470000   4.470000   66613100\n",
            "2014-01-16   4.500000   4.510000   4.320000   4.380000   4.380000   46975600\n",
            "2014-01-17   4.150000   4.350000   4.110000   4.180000   4.180000   45842700\n",
            "2014-01-21   4.310000   4.340000   4.130000   4.170000   4.170000   58702900\n",
            "2014-01-22   3.790000   3.820000   3.660000   3.670000   3.670000   86329500\n",
            "2014-01-23   3.670000   3.680000   3.580000   3.620000   3.620000   29844700\n",
            "2014-01-24   3.600000   3.610000   3.410000   3.470000   3.470000   48095800\n",
            "2014-01-27   3.480000   3.490000   3.350000   3.410000   3.410000   31332300\n",
            "2014-01-28   3.430000   3.570000   3.410000   3.540000   3.540000   27962600\n",
            "2014-01-29   3.520000   3.560000   3.450000   3.480000   3.480000   21765000\n",
            "2014-01-30   3.510000   3.520000   3.450000   3.480000   3.480000   17111700\n",
            "2014-01-31   3.450000   3.470000   3.430000   3.430000   3.430000   13602100\n",
            "2014-02-03   3.430000   3.440000   3.330000   3.330000   3.330000   22280200\n",
            "2014-02-04   3.350000   3.430000   3.300000   3.370000   3.370000   22681000\n",
            "2014-02-05   3.360000   3.370000   3.290000   3.310000   3.310000   14858900\n",
            "2014-02-06   3.320000   3.460000   3.320000   3.410000   3.410000   16072900\n",
            "2014-02-07   3.460000   3.500000   3.420000   3.470000   3.470000   23119700\n",
            "2014-02-10   3.500000   3.640000   3.450000   3.630000   3.630000   21241100\n",
            "2014-02-11   3.660000   3.750000   3.650000   3.700000   3.700000   34198100\n",
            "2014-02-12   3.710000   3.730000   3.630000   3.690000   3.690000   18321300\n",
            "2014-02-13   3.640000   3.710000   3.640000   3.700000   3.700000   13368600\n",
            "...               ...        ...        ...        ...        ...        ...\n",
            "2018-11-15  20.719999  21.770000  20.420000  21.490000  21.490000   97715500\n",
            "2018-11-16  19.870001  20.969999  19.719999  20.660000  20.660000  112376600\n",
            "2018-11-19  20.400000  20.590000  19.090000  19.110001  19.110001   93578200\n",
            "2018-11-20  17.400000  19.580000  17.180000  19.209999  19.209999  109869400\n",
            "2018-11-21  20.049999  20.309999  18.500000  18.730000  18.730000   81585600\n",
            "2018-11-23  18.610001  19.830000  18.559999  19.379999  19.379999   54611300\n",
            "2018-11-26  19.959999  20.190001  19.110001  20.080000  20.080000   83211000\n",
            "2018-11-27  19.770000  21.450001  19.730000  21.049999  21.049999  119230100\n",
            "2018-11-28  21.820000  21.879999  20.180000  21.340000  21.340000  134425300\n",
            "2018-11-29  21.190001  21.610001  20.730000  21.430000  21.430000   79853700\n",
            "2018-11-30  21.299999  21.360001  20.520000  21.299999  21.299999   82370700\n",
            "2018-12-03  22.480000  23.750000  22.370001  23.709999  23.709999  139607400\n",
            "2018-12-04  23.350000  23.420000  21.070000  21.120001  21.120001  127392900\n",
            "2018-12-06  20.219999  21.410000  20.059999  21.299999  21.299999  103434700\n",
            "2018-12-07  21.299999  21.379999  19.170000  19.459999  19.459999  105764500\n",
            "2018-12-10  19.350000  20.129999  19.270000  19.990000  19.990000   77984500\n",
            "2018-12-11  20.709999  21.139999  19.690001  19.980000  19.980000   88027400\n",
            "2018-12-12  20.320000  21.020000  19.709999  20.480000  20.480000  100340700\n",
            "2018-12-13  20.629999  20.870001  19.760000  19.860001  19.860001   88108300\n",
            "2018-12-14  19.580000  20.700001  19.520000  19.900000  19.900000   84713600\n",
            "2018-12-17  20.010000  20.020000  18.639999  18.830000  18.830000  115437900\n",
            "2018-12-18  19.150000  19.840000  18.879999  19.500000  19.500000  101512900\n",
            "2018-12-19  19.440001  19.719999  18.000000  18.160000  18.160000  120644500\n",
            "2018-12-20  18.110001  18.860001  17.340000  17.940001  17.940001  119394500\n",
            "2018-12-21  18.120001  18.340000  16.760000  16.930000  16.930000  132246000\n",
            "2018-12-24  16.520000  17.219999  16.370001  16.650000  16.650000   62933100\n",
            "2018-12-26  16.879999  17.910000  16.030001  17.900000  17.900000  108811800\n",
            "2018-12-27  17.430000  17.740000  16.440001  17.490000  17.490000  111373000\n",
            "2018-12-28  17.530001  18.309999  17.139999  17.820000  17.820000  109214400\n",
            "2018-12-31  18.150000  18.510000  17.850000  18.459999  18.459999   84732200\n",
            "\n",
            "[1258 rows x 6 columns]\n"
          ]
        }
      ],
      "execution_count": 18,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
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